Real-time online rescheduling for multiple agile satellites with emergent tasks

doi:10.23919/JSEE.2021.000120

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1407-1420.doi: 10.23919/JSEE.2021.000120

收稿日期:2020-10-22 接受日期:2021-11-08 出版日期:2022-01-05 发布日期:2022-01-05

Real-time online rescheduling for multiple agile satellites with emergent tasks

Jun WEN(), Xiaolu LIU(), Lei HE*()

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received:2020-10-22 Accepted:2021-11-08 Online:2022-01-05 Published:2022-01-05
Contact: Lei HE E-mail:jun_wen@aliyun.com;lxl_sunny@nudt.edu.cn;helei@nudt.edu.cn
About author:|WEN Jun was born in 1997. She received her B.S. degree in information and computing science from the School of Mathematics and Statistics, Changsha University of Science and Technology (CSUST), China, in 2019. She is currently a master student in management science and engineering at the College of Systems Engineering, National University of Defense Technology (NUDT), China. Her research interests are artificial intelligence, deep reinforcement learning, and metaheuristics, mainly focusing on distribution management and satellite scheduling problems such as collaborative mission planning methods of multi-satellite mobile target tracking. E-mail: jun_wen@aliyun.com||LIU Xiaolu was born in 1985. She received her B.E. degree in system engineering from National University of Defense Technology (NUDT), China, in 2006, and Ph.D. degree in management science and engineering from NUDT in 2011. From October 2015 to October 2016, she was a visiting scholar in école des Hautes études commerciales de Montréal (HEC), Montreal, Canada. She is currently an associate professor at the College of Systems Engineering, NUDT. Her research insterests are artificial intelligence and metaheuristics, focusing on distribution management and satellite scheduling problems. E-mail: lxl_sunny@nudt.edu.cn||HE Lei was born in 1991. He received his B.E. degree in management engineering from National University of Defense Technology (NUDT), China, in 2014, and Ph.D. degree in management science and engineering from NUDT in 2019. From October 2017 to October 2019, he was a visiting Ph.D. student in Delft University of Technology, the Netherlands. He is currently a lecturer at the College of Systems Engineering, NUDT. His research interests include artificial intelligence and machine learning. His current research focuses on the optimization of scheduling and planning of Earth observation satellites missions by designing exact and metaheuristic algorithms. E-mail: helei@nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (72001212, 71701204, 71801218) and the China Hunan Postgraduate Research Innovating Project (CX2018B020).

摘要/Abstract

Abstract:

The emergent task is a kind of uncertain event that satellite systems often encounter in the application process. In this paper, the multi-satellite distributed coordinating and scheduling problem considering emergent tasks is studied. Due to the limitation of onboard computational resources and time, common online onboard rescheduling methods for such problems usually adopt simple greedy methods, sacrificing the solution quality to deliver timely solutions. To better solve the problem, a new multi-satellite onboard scheduling and coordinating framework based on multi-solution integration is proposed. This method uses high computational power on the ground and generates multiple solutions, changing the complex onboard rescheduling problem to a solution selection problem. With this method, it is possible that little time is used to generate a solution that is as good as the solutions on the ground. We further propose several multi-satellite coordination methods based on the multi-agent Markov decision process (MMDP) and mixed-integer programming (MIP). These methods enable the satellite to make independent decisions and produce high-quality solutions. Compared with the traditional centralized scheduling method, the proposed distributed method reduces the cost of satellite communication and increases the response speed for emergent tasks. Extensive experiments show that the proposed multi-solution integration framework and the distributed coordinating strategies are efficient and effective for onboard scheduling considering emergent tasks.

Key words: agile satellite scheduling, emergent task, onboard rescheduling, distributed coordinating, multi-solution integration

. [J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1407-1420.

Jun WEN, Xiaolu LIU, Lei HE. Real-time online rescheduling for multiple agile satellites with emergent tasks[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1407-1420.

图/表 13

参考文献 43

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Satellite	$ax$	$ e $	$ic$	$ \omega $ /(°)	RAAN	$ m $
Satellite 1	7 200 000	0.000 627	96.576	0	175.72	0.075
Satellite 2	7 200 000	0.000 627	96.576	0	145.72	30.075
Satellite 3	7 200 000	0.000 627	96.576	0	115.72	60.075

Satellite	Number of tasks	FR	MMDPOP	GS	MIPS	SSR
1	100	1.6698	<0.0001	<0.0001	0.0082	<0.0001
1	200	4.0864	<0.0001	<0.0001	0.0134	<0.0001
1	300	7.0089	<0.0001	<0.0001	0.0174	<0.0001
1	400	9.9342	<0.0001	<0.0001	0.0238	<0.0001
2	100	1.4958	<0.0001	<0.0001	0.0087	<0.0001
2	200	3.9623	0.0001	0.0001	0.0171	<0.0001
2	300	7.2068	0.0001	0.0001	0.0280	0.0001
2	400	10.7444	0.0001	0.0001	0.0379	0.0001
3	100	1.4547	0.0001	0.0001	0.0086	<0.0001
3	200	3.5889	0.0001	0.0001	0.0183	0.0001
3	300	6.6247	0.0001	0.0001	0.0295	0.0001
3	400	10.1203	0.0002	0.0002	0.0460	0.0001
Average	?	5.6507	0.0001	0.0001	0.0239	0.0001

Satellite	Number of tasks	FR	MMDPOP	GS	MIPS	SSR
1	100	330.01	325.33	325.87	325.87	325.15
1	200	556.12	535.77	539.46	539.32	530.05
1	300	671.67	669.51	672.26	674.24	658.34
1	400	731.87	738.24	738.47	743.85	724.70
2	100	451.75	442.75	447.61	448.21	447.42
2	200	870.55	849.99	860.48	859.14	848.56
2	300	1190.67	1186.92	1185.50	1209.66	1153.84
2	400	1385.66	1415.30	1410.54	1443.27	1369.31
3	100	458.75	454.49	458.00	457.98	458.43
3	200	945.23	928.32	933.77	935.87	934.42
3	300	1446.54	1427.96	1435.84	1439.00	1419.59
3	400	1770.61	1785.36	1775.09	1809.00	1749.47
Average	?	900.78	896.66	898.57	907.12	884.94

Real-time online rescheduling for multiple agile satellites with emergent tasks

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